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CALSCALE:GREGORIAN
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UID:5d48454d-56a7-4b0d-8278-3f68d8fe162f@eventxplore
DTSTAMP:20260928T214530Z
DTSTART:20261016T185500Z
DTEND:20261016T201000Z
SUMMARY:BME7900 Seminar: Johannes Paetzold (Weill Cornell Medicine)
DESCRIPTION:Graphs and structure in training medical vision models Geometric concepts and structure are intuitive to humans but remain underused in neural network training\; for example\, we naturally understand the shortest path between two locations on a map (a graph problem) or recognize the shape of an object regardless of its orientation (a symmetry problem). Because these concepts are so intuitive\, I am convinced that geometry will continue to play a major role in computer vision. In particular\, geometric understanding can help align models to human preferences which is especially relevant in this era
LOCATION:Weill Hall
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